Abstract
Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artificial Intelligences decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.
Abstract (translated)
人工智能应用逐渐从研究实验室的安全边界外移，并入侵到我们的日常生活中。这同样适用于知识图谱上的机器学习方法，从21世纪初开始，它们的应用一直在稳步增长。然而，在许多应用中，用户需要了解人工智能的决策。这导致了对可解释人工智能的需求增加。知识图谱成为展示连接数据（即知识）在人类和机器可读方式下的有利土壤，因为它们具有将知识以人类和机器可读方式展示的能力。 本调查给可解释人工智能在知识图谱上的历史提供了简短回顾。此外，我们通过论证可解释人工智能与可解释机器学习的概念超载和重叠，引入了父概念可解释人工智能。这使得我们能够在调查中明确区分这两个概念，同时考虑它们的相似之处。因此，我们在调查中为知识图谱上的可解释人工智能提出了一个案例，包括知识图谱上的可解释机器学习和可解释人工智能。这导致了一个新颖的分类器，用于知识图谱上的可解释人工智能。 此外，对知识图谱上可解释人工智能的研究全面回顾以及它们在分类器中的位置进行了呈现。最后，为未来研究在知识图谱上可解释人工智能领域确定了研究空白。
URL
https://arxiv.org/abs/2404.03499
